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Proceedings ArticleDOI

Application of bee colony algorithm for FANET routing

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TLDR
Owing to the experimental analysis, bio-inspired algorithms based on the bee colony were proved to show good results, having better efficiency than traditional FANET routing algorithms in most cases.
Abstract
FANET are wireless ad hoc networks on unmanned aerial vehicles, and are characterized by high nodes mobility, dynamically changing topology and movement in 3D-space. FANET routing is an extremely complicated problem. The article describes the bee algorithm and the routing process based on the mentioned algorithm in ad hoc networks. The classification of FANET routing methods is given. The overview of the routing protocols based on the bee colony algorithms is provided. Owing to the experimental analysis, bio-inspired algorithms based on the bee colony were proved to show good results, having better efficiency than traditional FANET routing algorithms in most cases.

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Citations
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Journal ArticleDOI

A survey on position-based routing protocols for Flying Ad hoc Networks (FANETs)

TL;DR: A comprehensive survey of position-based routing protocols for FANETs with their various categories is proposed, including a classification and a taxonomy of these protocols, and a detailed description of the routing schemes used in each category.
Journal ArticleDOI

Routing in Flying Ad Hoc Networks: Survey, Constraints, and Future Challenge Perspectives

TL;DR: A comprehensive survey is presented covering the architecture, the constraints, the mobility models, the routing techniques, and the simulation tools dedicated to FANETs, better presenting the state of the art of this specific area of research.
Journal ArticleDOI

Localization and Clustering Based on Swarm Intelligence in UAV Networks for Emergency Communications

TL;DR: This work proposes a new 3-D SIL algorithm based on particle swarm optimization (PSO) that exploits the particle search space in a limited boundary by using the bounding box method and proposes an energy-efficient swarm-intelligence-based clustering (SIC) algorithm, in which the particle fitness function is exploited for interclusters distance, intracluster distance, residual energy, and geographic location.
Journal ArticleDOI

Routing in Flying Ad Hoc Networks: A Comprehensive Survey

TL;DR: This paper presents UAV classification, communication and application architectures, and an exhaustive survey of the existing routing protocols for flying ad hoc networks, and highlights the key features, strengths and weaknesses, and different mobility models used for the performance evaluation of theexisting FANET routing protocols.
Journal ArticleDOI

Robust and Reliable Predictive Routing Strategy for Flying Ad-Hoc Networks

TL;DR: This work proposes a combined omnidirectional and directional transmission scheme, together with dynamic angle adjustment, which features hybrid use of unicasting and geocasting routing using location and trajectory information for flying ad hoc networks.
References
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Journal ArticleDOI

Survey of Important Issues in UAV Communication Networks

TL;DR: This paper surveys the work done toward all of the outstanding issues, relating to this new class of networks, so as to spur further research in these areas.
Book ChapterDOI

Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems

TL;DR: The ABC algorithm has been extended for solving constrained optimization problems and applied to a set of constrained problems to show superior performance on these kind of problems.
Book ChapterDOI

The bees algorithm, a novel tool for complex optimisation problems

TL;DR: This chapter presents a new population-based search algorithm called the Bees Algorithm, which mimics the food foraging behavior of swarms of honeybees and can be used for both combinatorial optimization and functional optimization.
Journal ArticleDOI

Flying Ad-Hoc Networks (FANETs)

TL;DR: In this paper, Flying Ad-Hoc Networks (FANETs) are surveyed which is an ad hoc network connecting the UAVs, and the main FANET design challenges are introduced.
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